The invention relates to the general field of wastewater networks. The invention relates in particular to real time management of such a network.
A wastewater network typically comprises water transport works, e.g. pipes, for conveying water to a treatment plant, and storage works, such as storm-water tanks. The network may also include automatic means and actuators such as pumps and valves for influencing the flow of water in the network. For example, a pump may be controlled as a function of the level of water in a tank.
The control setpoints of the actuators influence the performance of the network. For example, a high trigger level for a pump for discharging a storm tank serves to limit the quantity of water that is discharged into the network downstream and thus to limit the risk of flooding or of overflowing into the natural environment from the network downstream. Nevertheless, such a high level also puts a limit on the quantity of water that can still be stored in the event of heavy rain. The risk of overflowing into the natural environment upstream from the storm tank is thus increased.
Real time management of a wastewater network consists in adapting the setpoints for controlling the actuators to a rain event, so as to improve the performance of the network. By way of example, network performance may be characterized by the locations of floods in built-up areas and by the quantity of water that overflows into the natural environment, or indeed the quantity of energy that is used while performing said management. Thus, it is known to adapt control setpoints for actuators to match rain as forecast or measured.
For example, the Seine-Saint-Denis drainage network as described in the document “Exploitation en temps réel du réseau d'assainissement de Seine-Saint-Denis” [Real time operation of the Seine-Saint-Denis drainage network] by J. M. Delattre, as given to the Congress “La gestion avanzada del drenaje urbano” [Advanced management of urban drainage], Barcelona, 2004, is based on a scenario approach. In that approach, a rain type approximating as closely as possible to present or future real rain over the territory is selected from a sample of 27 rain types. Each rain type corresponds to a set of setpoints for actuators of the network. The sets of setpoints were predetermined, by using a model of the network.
It is also known to use an optimization algorithm to predetermine an optimum set of setpoints for a given rain type, as a function of the network model. Thus, the document “Optimization of sewer networks hydraulic behavior during wet weather: coupling genetic algorithms with two sewer networks modeling tools” presented at the Novatech 2010 Congress at Lyon, shows that such optimization makes it possible to improve the performance of real time management compared with predetermined setpoints derived from the long experience of the network manager.